1-June
Synch |
Introduction/What is Machine Learning?
Readings
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2-June
Synch |
Getting Started with Machine Learning
Readings
- Ch 1: “Introduction” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 1–25.
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3-June
Asynch |
DataCamp Modules:
- Introduction to Python course (If Needed)
- “Introduction to AI” from AI Fundamentals course
- “Data Manipulation with pandas
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- Transforming Data
- Aggregating Data
- Slicing and Indexing
- Creating and Visualizing DataFrames (Optional)
- Writing Efficient Code with pandas (Optional)
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7-June
Synch |
Inspecting Data
Readings
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8-June
Synch |
Representing Data
Readings
- Ch 4: “Representing Data/Engineering Features” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 213–55
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9-June
Synch |
Evaluation Methods
Readings
- Ch 5: “Model Evaluation and Improvement” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 213–55
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10-June
Async |
DataCamp Modules
- Pre-processing for Machine Learning in Python course
- Model Validation in Python course
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14-June
Asynch |
Videos (K-nearest Neighbors and Linear Models)
password is course number (no spaces)
DataCamp Modules
- “Basic Modeling in scikit-learn (through Feature Importances)” (In Model Validation in Python course)
- “Classification” (in Supervised Learning with scikit-learn course)
- “Regression” (in Supervised Learning with scikit-learn course)
- “Cross Validation” (In Model Validation in Python course)
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15-June
Synch |
Supervised Learning (k-Nearest Neighbors)
Readings
- Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 27–46
Supervised Learning (Linear Models)
Readings
- Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 47–70
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16-June
Asynch |
Videos (Naive Bayes Classifiers and Decision Trees & Support Vector Machines and Uncertainty estimates from Classifiers)
password is course number (no spaces)
DataCamp Modules
- “Classification and Regression Trees” (in Machine Learning with Tree-Based Models in Python course)
- Linear Classifiers in Python course
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17-June
Synch |
Supervised Learning (Naive Bayes Classifiers and Decision Trees)
Readings
- Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 70–94
Supervised Learning (Support Vector Machines and Uncertainty estimates from Classifiers)
Readings
- Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 93–106 and 121–131
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21-June
Asynch |
Unsupervised Learning (Dimensionality Reduction & Feature Extraction, and Manifold Learning)
password is course number (no spaces)
DataCamp Modules
- “Clustering for dataset exploration” (in Unsupervised Learning in Python course)
- “Visualization with hierarchical clustering and t-SNE” (in Unsupervised Learning in Python course)
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22-June
Synch |
Unsupervised Learning (Dimensionality Reduction & Feature Extraction, and Manifold Learning)
Readings
- Ch 3: “Unsupervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 133–170
Ethics
Readings
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23-June
Asynch |
Unsupervised Learning (Clustering)
password is course number (no spaces)
DataCamp:
(1) “Decorrelating your data and dimension reduction“ (in Unsupervised Learning in Python course)
(2) “Discovering interpretable features” (in Unsupervised Learning in Python course) |
24-June
Synch |
Unsupervised Learning (Clustering)
Readings
- Ch 3: “Unsupervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 170–211
Ethics
Readings
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